Miscellaneous simulation functions

Define several simulation functions to be used in the demos of the package.

simulation_cor(group, cor_group, v = 1)

simulation_X(N, Cor)

simulation_DATA(X, supp, minB, maxB, stn)

compsim(x, ...)

# S3 method for simuls
compsim(x, result.boost, level = 1, ...)

Arguments

group

A numeric vector. Group membership of each of the variables.

cor_group

A numeric vector. Intra-group Pearson correlation.

v

A numeric value. The diagonal value of the generated matrix.

N

A numeric value. The number of observations.

Cor

A numeric matrix. A correlation matrix to be used for random sampling.

X

A numeric matrix. Observations*variables.

supp

A numeric vector. The true predictors.

minB

A numeric value. Minimum absolute value for a beta coefficient.

maxB

A numeric value. Maximum absolute value for a beta coefficient.

stn

A numeric value. A scaling factor for the noise in the response. The higher, the smaller the noise.

x

List. Simulated dataset.

...

For compatibility issues.

result.boost

Row matrix of numerical value. Result of selecboost for a given c0.

level

List. Threshold for proportions of selected variables.

Value

simulation_cor returns a numeric matrix.

simulation_X returns a numeric matrix.

simulation_DATA returns a list.

compsim.simuls returns a numerical vector.

Details

simulation_cor returns a numeric symetric matrix c whose order is the number of variables. An entry \(c_{i,j}\) is equal to

• \(i=j\), entries on the diagonal are equal to the v value

• \(i<>j\), 0 if the variable i and j do not belong to the same group

• \(i<>j\), cor_group[k] if the variable i and j belong to the group k

simulation_X returns a numeric matrix of replicates (by row) of random samples generated according to the Cor matrix.

simulation_DATA returns a list with the X matrix, the response vector Y, the true predictors, the beta coefficients, the scaling factor and the standard deviation.

compsim.simuls computes recall (sensitivity), precision (positive predictive value), and several Fscores (non-weighted Fscore, F1/2 and F2 weighted Fscores).

References

selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets, Frédéric Bertrand, Ismaïl Aouadi, Nicolas Jung, Raphael Carapito, Laurent Vallat, Seiamak Bahram, Myriam Maumy-Bertrand, Bioinformatics, 2020. doi:10.1093/bioinformatics/btaa855

See also

glmnet, cv.glmnet, AICc_BIC_glmnetB, lars, cv.lars, msgps

Author

Frederic Bertrand, frederic.bertrand@utt.fr with contributions from Nicolas Jung.

Examples

N<-10
group<-c(rep(1:2,5))
cor_group<-c(.8,.4)
supp<-c(1,1,1,0,0,0,0,0,0,0)
minB<-1
maxB<-2
stn<-5
C<-simulation_cor(group,cor_group)

set.seed(314)
X<-simulation_X(10,C)
G<-abs(cor(X))
hist(G[lower.tri(G)])


set.seed(314)
DATA_exemple<-simulation_DATA(X,supp,1,2,stn)

set.seed(314)
result.boost = fastboost(DATA_exemple$X, DATA_exemple$Y, steps.seq = .7, c0lim = FALSE,
use.parallel = FALSE, B=10)
compsim(DATA_exemple, result.boost, level=.7)
#> [1] 0.6666667 0.2857143 0.4000000 0.3225806 0.5263158 1.0000000